Screening and identification of hub genes in pancreatic cancer by integrated bioinformatics analysis

Exploring the oncogenic role of RGS19 in bladder cancer progression and prognosis

This study investigates the role of autophagy-related genes (ARGs) in bladder cancer (BLCA), focusing on the regulator of G protein signaling 19 (RGS19). Using data from The Cancer Genome Atlas (TCGA) and the Human Autophagy Database (HADb), we identified RGS19 as significantly upregulated and linked to poor prognosis in BLCA. Kaplan-Meier survival analysis confirmed its association with increased mortality and. In vitro, RGS19 knockdown in BLCA cell lines inhibited proliferation, migration, and invasion, while inducing apoptosis and autophagy. Transmission electron microscopy showed autophagic structures in RGS19-silenced cells. In vivo, a xenograft mouse model demonstrated reduced tumor growth with RGS19 knockdown. Immunohistochemical (IHC) analysis revealed decreased Ki67 and increased autophagy markers in tumors with reduced RGS19. Pathway analysis suggested RGS19 acts through the cGMP-PKG signaling pathway, validated by altered expression of soluble guanylate cyclase (sGC), protein kinase G (PKG1), phosphodiesterase 5 A (PDE5A), vasodilator-stimulated phosphoprotein (VASP), and phosphorylated VASP (p-VASP) upon RGS19 knockdown. These results highlight RGS19 as a potential biomarker and therapeutic target in BLCA.

Identification of potential biomarkers for pancreatic ductal adenocarcinoma: a bioinformatics analysis

PDA is an aggressive cancer with a 5-year survival rate, which is very low. There is no effective prognosis or therapy for PDA because of the lack of target biomarkers. The objective of this article is to identify the target biomarkers for PDA using a bioinformatics approach. In this work, we have analysed the three microarray datasets from the NCBI GEO database. We used the Geo2R tool to analyse the microarray data with the Benjamini and Hochberg false discovery rate method, and the significance level cut-off was set to 0.05. We have identified 659 DEGs from the datasets. There are a total of 15 hub genes that were selected from the PPI network constructed using the STRING application. Furthermore, these 15 genes were evaluated on PDA patients using TCGA and GTEx databases in (GEPIA). The online tool DAVID was used to analyse the functional annotation information for the DEGs. The functional pathway enrichment was performed on the GO and KEGG. The hub genes were mainly enriched for cell division, chromosome segregation, protein binding and microtubule binding. Further, the gene alteration study was performed using the cBioportal tool and screened out six hub genes (ASPM, CENPF, BIRC5, TTK, DLGAP5, and TOP2A) with a high alteration rate in PDA samples. Furthermore, Kaplan-Meier survival analysis was performed on the six hub genes and identified poor-survival outcomes that may be involved in tumorigenesis and PDA development. So, this study concludes that, these six hub genes may be potential prognostic biomarkers for PDA.

Anticipation and Verification of Dendrobium-Derived Nanovesicles for Skin Wound Healing Targets, Predicated Upon Immune Infiltration and Senescence

Background

Dendrobium, with profound botanical importance, reveals a rich composition of bioactive compounds, including polysaccharides, flavonoids, alkaloids, and diverse amino acids, holding promise for skin regeneration. However, the precise mechanism remains elusive. Seeking a potent natural remedy for wound healing, exocyst vesicles were successfully isolated from Dendrobium.

Aims of the Study

This investigation aimed to employ bioinformatics and in vivo experiments to elucidate target genes of Dendrobium-derived nanovesicles in skin wound healing, focusing on immune infiltration and senescence characteristics.

Materials and Methods

C57 mice experienced facilitated wound healing through Dendrobium-derived nanovesicles (DDNVs). Bioinformatics analysis and GEO database mining identified crucial genes by intersecting immune-related, senescence-related, and PANoptosis-associated genes. The identified genes underwent in vivo validation.

Results

DDNVs remarkably accelerated skin wound healing in C57 mice. Bioinformatics analysis revealed abnormal expression patterns of immune-related, senescence-related, and pan-apoptosis-related genes, highlighting an overexpressed IL-1β and downregulated IL-18 in the model group, Exploration of signaling pathways included IL-17, NF-kappa B, NOD-like receptor, and Toll-like receptor pathways. In vivo experiments confirmed DDNVs' efficacy in suppressing IL-1β expression, enhancing wound healing.

Conclusion

Plant-derived nanovesicles (PDNV) emerged as a natural, reliable, and productive approach to wound healing. DDNVs uptake by mouse skin tissues, labeled with a fluorescent dye, led to enhanced wound healing in C57 mice. Notably, IL-1β overexpression in immune cells and genes played a key role. DDNVs intervention effectively suppressed IL-1β expression, accelerating skin wound tissue repair.

TFRC, associated with hypoxia and immune, is a prognostic factor and potential therapeutic target for bladder cancer

BACKGROUND

Bladder cancer is a common malignancy of the urinary system, and the survival rate and recurrence rate of patients with muscular aggressive (MIBC) bladder cancer are not ideal. Hypoxia is a pathological process in which cells acquire special characteristics to adapt to anoxic environment, which can directly affect the proliferation, invasion and immune response of bladder cancer cells. Understanding the exact effects of hypoxia and immune-related genes in BLCA is helpful for early assessment of the prognosis of BLCA. However, the prognostic model of BLCA based on hypoxia and immune-related genes has not been reported.

PURPOSE

Hypoxia and immune cell have important role in the prognosis of bladder cancer (BLCA). The aim of this study was to investigate whether hypoxia and immune related genes could be a novel tools to predict the overall survival and immunotherapy of BLCA patients.

METHODS

First, we downloaded transcriptomic data and clinical information of BLCA patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A combined hypoxia and immune signature was then constructed on the basis of the training cohort via least absolute shrinkage and selection operator (LASSO) analysis and validated in test cohort. Afterwards, Kaplan-Meier curves, univariate and multivariate Cox and subgroup analysis were employed to assess the accuracy of our signature. Immune cell infiltration, checkpoint and the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm were used to investigate the immune environment and immunotherapy of BLCA patients. Furthermore, we confirmed the role of TFRC in bladder cancer cell lines T24 and UMUC-3 through cell experiments.

RESULTS

A combined hypoxia and immune signature containing 8 genes were successfully established. High-risk group in both training and test cohorts had significantly poorer OS than low-risk group. Univariate and multivariate Cox analysis indicated our signature could be regarded as an independent prognostic factor. Different checkpoint was differently expressed between two groups, including CTLA4, HAVCR2, LAG3, PD-L1 and PDCD1. TIDE analysis indicated high-risk patients had poor response to immunotherapy and easier to have immune escape. The drug sensitivity analysis showed that high-risk group patients were more potentially sensitive to many drugs. Meanwhile, TFRC could inhibit the proliferation and invasion ability of T24 and UMUC-3 cells.

CONCLUSION

A combined hypoxia and immune-related gene could be a novel predictive model for OS and immunotherapy estimation of BLCA patients and TFRC could be used as a potential therapeutic target in the future.

Cancer-associated fibroblast-related prognostic signature predicts prognosis and immunotherapy response in pancreatic adenocarcinoma based on single-cell and bulk RNA-sequencing

Cancer-associated fibroblasts (CAFs) influence many aspects of pancreatic adenocarcinoma (PAAD) carcinogenesis, including tumor cell proliferation, angiogenesis, invasion, and metastasis. A six-gene prognostic signature was constructed for PAAD based on the 189 CAF marker genes identified in single-cell RNA-sequencing data. Multivariate analyses showed that the risk score was independently prognostic for survival in the TCGA (P < 0.001) and ICGC (P = 0.004) cohorts. Tumor infiltration of CD8 T (P = 0.005) cells and naïve B cells (P = 0.001) was greater in the low-risk than in the high-risk group, with infiltration of these cells negatively correlated with risk score. Moreover, the TMB score was lower in the low-risk than in the high-risk group (P = 0.0051). Importantly, patients in low-risk group had better immunotherapy responses than in the high-risk group in an independent immunotherapy cohort (IMvigor210) (P = 0.039). The CAV1 and SOD3 were highly expressed in CAFs of PAAD tissues, which revealed by immunohistochemical staining. In summary, this comprehensive analysis resulted in the development of a novel prognostic signature, which was associated with immune cell infiltration, drug sensitivity, and TMB, and could predict the prognosis and immunotherapy response of patients with PAAD.

Analysis of Long Noncoding RNAs-Related Regulatory Mechanisms in Duchenne Muscular Dystrophy Using a Disease-Related lncRNA-mRNA Pathway Network

Objective

This study aimed to investigate the molecular regulatory mechanisms underpinning Duchenne muscular dystrophy (DMD).

Methods

Using microarray data, differentially expressed long noncoding RNAs (DELs) and DMD-related differentially expressed mRNAs (DEMs) were screened based on the comparative toxicogenomics database, using a cutoff of |log₂ fold change| > 1 and false discovery rate (FDR) < 0.05. Then, protein-protein interaction (PPI), coexpression network of lncRNA-mRNA, and DMD-related lncRNA-mRNA pathway networks were constructed, and functional analyses of the genes in the network were performed. Finally, the proportions of immune cells infiltrating the muscle tissues in DMD were analyzed, and the correlation between the immune cells and expression of the DELs/DEMs was studied.

Results

A total of 46 DELs and 313 DMD-related DEMs were identified. The PPI network revealed STAT1, VEGFA, and CCL2 to be the top three hub genes. The DMD-related lncRNA-mRNA pathway network comprising two pathways, nine DELs, and nine DMD-related DEMs showed that PYCARD, RIPK2, and CASP1 were significantly enriched in the NOD-like receptor signaling pathway, whereas MAP2K2, LUM, RPS6, PDCD4, TWIST1, and HIF1A were significantly enriched with proteoglycans in cancers. The nine DELs in this network were DBET, MBNL1-AS1, MIR29B2CHG, CCDC18-AS1, FAM111A-DT, GAS5, LINC01290, ATP2B1-AS1, and PSMB8-AS1.

Conclusion

The nine DMD-related DEMs and DELs identified in this study may play important roles in the occurrence and progression of DMD through the two pathways of the NOD-like receptor signaling pathway and proteoglycans in cancers.

Identification of Candidate Therapeutic Target Genes and Profiling of Tumor-Infiltrating Immune Cells in Pancreatic Cancer via Integrated Transcriptomic Analysis

Pancreatic cancer (PC) has a dismal prognosis despite advancing scientific and technological knowledge. The exploration of novel genes is critical to improving current therapeutic measures. This research is aimed at selecting hub genes that can act as candidate therapeutic target genes and as prognostic biomarkers in PC. Gene expression profiles of datasets GSE101448, GSE15471, and GSE62452 were extracted from the GEO database. The “limma” package was performed to select differentially expressed genes (DEGs) between PC and normal tissue samples in each dataset. Robust rank aggregation (RRA) algorithm was conducted to integrate multiple expression profiles and identify robust DEGs. GO analysis and KEGG analysis were conducted to identify the functional correlation of the DEGs. The CIBERSORT algorithm was conducted to estimate the immune cell composition of each tissue sample. STRING and Cytoscape were used to establish the protein-protein interaction (PPI) network. The cytoHubba plugin in Cytoscape was performed to identify hub genes. Survival analysis based on hub gene expression was performed with clinical information from TCGA database. 566 robust DEGs (338 upregulated genes and 226 downregulated genes) were identified. Tumor tissue had a higher infiltration of resting dendritic cells and tumor-associated macrophages (TAM), including M0, M1, and M2 macrophages, while infiltration levels of B memory cells, plasma cells, T cells CD8, T follicular helper cells, and NK cells in normal tissue were relatively higher. GO terms and KEGG pathway analysis results revealed enrichment in tumor-associated pathways, including the extracellular matrix organization, cell−substrate adhesion cytokine−cytokine receptor interaction, calcium signaling pathway, and glycine, serine, and threonine metabolism, to name a few. Finally, FN1, MSLN, PLAU, and VCAN were selected as hub genes. High expression of FN1, MSLN, PLAU, and VCAN in PC significantly correlated with poor prognosis. Integrated transcriptomic analysis was used to provide new insights into PC pathogenesis. FN1, MSLN, PLAU, and VCAN may be considered as novel biomarkers of PC.

Vitamin K2 Improves Osteogenic Differentiation by Inhibiting STAT1 via the Bcl-6 and IL-6/JAK in C3H10 T1/2 Clone 8 Cells

Osteogenic activity of vitamin K2 (VK2), a small molecular nutrient, has been suggested. However, the underlying mechanisms have not been fully elucidated. Therefore, this study aimed to explore the mechanisms by which VK2 promotes osteogenic differentiation. The effects of VK2 on osteogenic differentiation indicators were determined in C3H10 T1/2 clone 8 cells. The RNA-seq analysis was used to explore the hypothesis that VK2 promotes osteogenic differentiation. Small interfering RNA (siRNA) assay and plasmid transfection assay were used to determine the potential role of VK2 in the modulation of Bcl-6/STAT axis and IL-6/JAK/STAT signaling pathway. VK2 significantly increased alkaline phosphatase (ALP) activity, ALP, osteocalcin (OCN), and RUNX2 abundance, and RUNX2 protein expression. RNA-seq analysis showed that there were 314 differentially expressed genes (DEGs) upregulated and 1348 DEGs downregulated by VK2. PPI analysis determined the top 10 hub genes upregulated or downregulated by VK2. Overexpression of Bcl-6 increased osteogenic differentiation and decreased expression of STAT1. Administration with VK2 restored the inhibition by siBcl-6 in osteogenic differentiation. Knockdown of IL-6 decreased the mRNA levels of genes associated with the JAK/STAT signaling pathway, and increased markers of osteoblast differentiation. Furthermore, treatment with VK2 improved inhibition in osteogenic differentiation and decreased enhancement of JAK/STAT signaling pathway related genes by overexpression of IL-6. Our study suggests that VK2 could improve osteogenic differentiation via the Bcl-6/STAT axis and IL-6/JAK/STAT signaling pathway.

An Integrated Analysis of Prognostic Signature and Immune Microenvironment in Tongue Squamous Cell Carcinoma

Tongue squamous cell carcinoma (TSCC) is a prevalent cancer of the oral cavity. Survival metrics are usually unsatisfactory, even using combined treatment with surgery, radiation, and chemotherapy. Immune checkpoint inhibitors can prolong survival, especially in patients with recurrent or metastatic disease. However, there are few effective biomarkers to provide prognosis and guide immunotherapy. Here, we utilized weighted gene co-expression network analysis to identify the co-expression module and selected the turquoise module for further scrutiny. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed the innate pathways. The findings indicated that cell junction organization, response to topologically incorrect protein, and regulation of cell adhesion pathways may be essential. Eleven crucial predictive genes (PLXNB1, N4BP3, KDELR2, INTS8, PLAU, PPFIBP2, OAF, LMF1, IL34, ZFP3, and MAP7D3) were used to establish a risk model based on Cox and LASSO analyses of The Cancer Genome Atlas and GSE65858 databases (regarding overall survival). Kaplan–Meier analysis and receiver operating characteristic curve suggested that the risk model had better prognostic effectiveness than other clinical traits. Consensus clustering was used to classify TSCC samples into two groups with significantly different survival rates. ESTIMATE and CIBERSORT were used to display the immune landscape of TSCC and indicate the stromal score; specific types of immune cells, including naïve B cells, plasma cells, CD8 T cells, CD4 memory resting and memory activated T cells, follicular helper T cells, and T regulatory cells, may influence the heterogeneous immune microenvironment in TSCC. To further identify hub genes, we downloaded GEO datasets (GSE41613 and GSE31056) and successfully validated the risk model. Two hub genes (PLAU and PPFIBP2) were strongly associated with CD4+ and CD8+ T cells and programmed cell death protein 1 (PD1) and PD-ligand 1.

ARG1 as a promising biomarker for sepsis diagnosis and prognosis: evidence from WGCNA and PPI network

Background

Sepsis is a life-threatening multi-organ dysfunction caused by the dysregulated host response to infection. Sepsis remains a major global concern with high mortality and morbidity, while management of sepsis patients relies heavily on early recognition and rapid stratification. This study aims to identify the crucial genes and biomarkers for sepsis which could guide clinicians to make rapid diagnosis and prognostication.

Methods

Preliminary analysis of multiple global datasets, including 170 samples from patients with sepsis and 110 healthy control samples, revealed common differentially expressed genes (DEGs) in peripheral blood of patients with sepsis. After Gene Oncology (GO) and pathway analysis, the Weighted Gene Correlation Network Analysis (WGCNA) was used to screen for genes most related with clinical diagnosis. Also, the Protein-Protein Interaction Network (PPI Network) was constructed based on the DEGs and the hub genes were found. The results of WGCNA and PPI network were compared and one shared gene was discovered. Then more datasets of 728 experimental samples and 355 control samples were used to prove the diagnostic and prognostic value of this gene. Last, we used real-time PCR to confirm the bioinformatic results.

Results

Four hundred forty-four common differentially expressed genes in the blood of sepsis patients from different ethnicities were identified. Fifteen genes most related with clinical diagnosis were found by WGCNA, and 24 hub genes with most node degrees were identified by PPI network. ARG1 turned out to be the unique overlapped gene. Further analysis using more datasets showed that ARG1 was not only sharply up-regulated in sepsis than in healthy controls, but also significantly high-expressed in septic shock than in non-septic shock, significantly high-expressed in severe or lethal sepsis than in uncomplicated sepsis, and significantly high-expressed in non-responders than in responders upon early treatment. These all demonstrate the performance of ARG1 as a key biomarker. Last, the up-regulation of ARG1 in the blood was confirmed experimentally.

Conclusions

We identified crucial genes that may play significant roles in sepsis by WGCNA and PPI network. ARG1 was the only overlapped gene in both results and could be used to make an accurate diagnosis, discriminate the severity and predict the treatment response of sepsis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41065-022-00240-1.

Proteomic profiling identifies novel diagnostic biomarkers and molecular subtypes for mucinous tubular and spindle cell carcinoma of the kidney

Mucinous tubular and spindle cell carcinoma (MTSCC) is a relatively rare renal epithelial neoplasm resembling type 1 papillary renal cell carcinoma (PRCC) morphologically and immunohistochemically. The accurate diagnosis of MTSCC remains a challenge. Here, by using proteomic profiling, we characterized MTSCC and PRCC to identify diagnostic biomarkers. We found that the MTSCC tumor proteome was significantly enriched in B‐cell‐mediated immunity when compared with the proteome of adjacent normal tissues of MTSCC or tumors of PRCC. Importantly, we identified MZB1, VCAN, and SOSTDC1 as diagnostic biomarkers to distinguish MTSCC from the solid variant of type 1 PRCC, with an AUC of 0.985 when combined. MZB1 was inversely correlated with tumor clinical stage and may play an anti‐tumor role by activating the complement system. Finally, unsupervised clustering revealed two molecular subtypes of MTSCC, displaying different morphology, expression signatures of oxidative phosphorylation, and aggravation. In summary, our analyses identified a three‐protein diagnostic panel and molecular subtypes for MTSCC. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Role of Hub Genes in the Occurrence and Development of Testicular Cancer Based on Bioinformatics

Background

Testicular cancer severely affects male health, so finding effective diagnosis and prognostic indicators and exploring its pathogenesis are very important.

Purpose

This study aims to explore the hub genes that play important roles in the occurrence and development of testicular germ cell tumor (TGCT).

Methods

Data were obtained from Gene Expression Omnibus datasets (GSE3218 and GSE1818) and verified in The Cancer Genome Atlas database and the Genotype-Tissue Expression database and the Human Protein Atlas database. A protein–protein interaction network was constructed to obtain hub genes. GEO2R, R software and packages were used to analyze differentially expressed genes (DEGs), receiver operating characteristic curve assessment, Cox regression analysis, Kaplan–Meier survival curve assessment, Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes analysis, the relationship with clinicopathological information, gene set enrichment analysis, the correlation with immune cells’ infiltration, and the expression in pan-cancers of the hub genes.

Results

PLK4, TRIP13, TPR, KIF18A, CDKN3, HMMR, PBK, PTTG1, CKS2, SYCP1, HSPA2, and MKI67 were selected as the hub genes. mRNA of PLK4, TRIP13, CDKN3, SYCP1, HSPA2, and MKI67 had high diagnostic values, and higher expression of CDKN3 and HSPA2 mRNA were poor prognostic factors for progression-free interval of TGCT. The hub genes involved organelle division and cell cycle, chromosome and centromeric region, heat shock protein binding, and more. Downregulated TPR and PLK4 were selected as research targets for continued study, and they may participate in multiple signaling pathways. The expression of TPR and PLK4 correlated with the infiltration of a variety of immune cells and differed in pan-cancers.

Conclusion

The mRNA levels of multiple hub genes have high diagnostic and prognostic values for TGCT. TPR and PLK4 may play a role in the occurrence and development of TGCT through cancer-related signaling pathways.

Identification of potential core genes in gastric cancer using bioinformatics analysis

Background

Gastric cancer is the third leading cause of cancer-related mortality in China. Most patients with gastric cancer have no obvious early symptoms; thus, many of them are in the middle and late stages of gastric cancer at first diagnosis and miss the best treatment opportunity. Molecular targeted therapy is particularly important in changing this status quo.

Methods

Three microarray datasets (GSE29272, GSE33651, and GSE54129) were selected from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened using GEO2R. The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to analyze the functional features of these DEGs and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape software. The expressions of hub genes were evaluated based on Gene Expression Profiling Interactive Analysis (GEPIA). Moreover, we used the online Kaplan-Meier plotter survival analysis tool to evaluate the prognostic values of hub genes. The Target Scan database was used to predict microRNAs that could regulate the target gene, collagen type IV alpha 1 chain (COL4A1). The OncomiR database was used to analyze the expression levels of three microRNAs, as well as the relationships with tumor stage, grade, and prognosis.

Results

We identified 78 DEGs, including 53 upregulated genes and 25 downregulated genes. The DEGs were mainly enriched in extracellular matrix organization, extracellular structure organization, and response to wounding. Moreover, three KEGG pathways were markedly enriched, including focal adhesion, complement and coagulation cascades, and extracellular matrix (ECM)-receptor interaction. Among these 78 genes, we selected 10 hub genes. The overexpression levels of these hub genes were closely related to poor prognosis and the development of gastric cancer (except for COL3A1, LOX, and CXCL8). Moreover, we found that microRNA-29a-3p, miR-29b-3p, and miR-29c-3p were the potential microRNAs that could regulate the target gene, COL4A1.

Conclusions

Our results showed that FN1, COL1A1, TIMP1, COL1A2, SPARC, COL4A1, and SERPINE1 could contribute to the development of novel molecular targets and biomarker-driven treatments for gastric cancer.

Implications of prognosis-associated genes in pancreatic tumor metastasis: lessons from global studies in bioinformatics

Pancreatic cancer (PC) is a highly lethal malignancy with a 5-year survival rate of 10%. The occurrence of metastasis, among other hallmarks, is the main contributor to its poor prognosis. Consequently, the elucidation of metastatic genes involved in the aggressive nature of the disease and its poor prognosis will result in the development of new treatment modalities for improved management of PC. There is a deep interest in understanding underlying disease pathology, identifying key prognostic genes, and genes associated with metastasis. Computational approaches, which have become increasingly relevant over the last decade, are commonly used to explore such interests. This review aims to address global studies that have employed global approaches to identify prognostic and metastatic genes, while highlighting their methods and limitations. A panel of 48 prognostic genes were identified across these studies, but only five, including ANLN, ARNTL2, PLAU, TOP2A, and VCAN, were validated in multiple studies and associated with metastasis. Their association with metastasis has been further explored here, and the implications of these genes in the metastatic cascade have been interpreted.

PLAU Promotes Cell Proliferation and Epithelial-Mesenchymal Transition in Head and Neck Squamous Cell Carcinoma

Plasminogen activator, urokinase (uPA) is a secreted serine protease whose Dysregulation is often accompanied by various cancers. However, the biological functions and potential mechanisms of PLAU in head and neck squamous cell carcinoma (HNSCC) remain undetermined. Here, the expression, prognosis, function, and coexpression genetic networks of PLAU in HNSCC were investigated by a series of public bioinformatics tools. A Higher PLAU level predicted a poorer clinical outcome. Meanwhile, functional network analysis implied that PLAU and associated genes mainly regulated cell-substrate adhesion, tissue migration, and extracellular matrix binding. The top 4 significantly associated genes are C10orf55, ITGA5, SERPINE1, and TNFRSF12A. Pathway enrichment analysis indicated that PLAU might activate the epithelial-to-mesenchymal transition (EMT) process, which could explain the poor prognosis in HNSCC. Besides, genes associated with PLAU were also enriched in EMT pathways. We further validated the bioinformatics analysis results by in vivo and in vitro experiments. Then, we found that much more PLAU was detected in HNSCC tissues, and the silencing of PLAU inhibit the proliferation, migration, and EMT process of CAL27 cell lines. Notably, the downregulation of PLAU decreased the expression of TNFRSF12A. Moreover, knockdown TNFRSF12A also inhibits cell proliferation and migration. In vivo experiment results indicated that PLAU inhibition could suppress tumor growth. Collectively, PLAU is necessary for tumor progression and can be a diagnostic and prognostic biomarker in HNSCC.

Identification of Hub Prognosis-Associated Oxidative Stress Genes in Pancreatic Cancer Using Integrated Bioinformatics Analysis

Background

Intratumoral oxidative stress (OS) has been associated with the progression of various tumors. However, OS has not been considered a candidate therapeutic target for pancreatic cancer (PC) owing to the lack of validated biomarkers.

Methods

We compared gene expression profiles of PC samples and the transcriptome data of normal pancreas tissues from The Cancer Genome Atlas (TCGA) and Genome Tissue Expression (GTEx) databases to identify differentially expressed OS genes in PC. PC patients’ gene profile from the Gene Expression Omnibus (GEO) database was used as a validation cohort.

Results

A total of 148 differentially expressed OS-related genes in PC were used to construct a protein-protein interaction network. Univariate Cox regression analysis, least absolute shrinkage, selection operator analysis revealed seven hub prognosis-associated OS genes that served to construct a prognostic risk model. Based on integrated bioinformatics analyses, our prognostic model, whose diagnostic accuracy was validated in both cohorts, reliably predicted the overall survival of patients with PC and cancer progression. Further analysis revealed significant associations between seven hub gene expression levels and patient outcomes, which were validated at the protein level using the Human Protein Atlas database. A nomogram based on the expression of these seven hub genes exhibited prognostic value in PC.

Conclusion

Our study provides novel insights into PC pathogenesis and provides new genetic markers for prognosis prediction and clinical treatment personalization for PC patients.

Identification and Validation of Novel Genes in Anaplastic Thyroid Carcinoma via Bioinformatics Analysis

Purpose

The conventional interventions of anaplastic thyroid carcinoma (ATC) patients are mainly through surgery, chemotherapy, and radiotherapy; however, it is hardly to improve survival rate. We aimed to investigate the differential expressed genes (DEGs) between ATC and normal thyroid gland through bioinformatics analysis of the microarray datasets and find new potential therapeutic targets for ATC.

Methods

Microarray datasets GSE9115, GSE29265, GSE33630, GSE53072, and GSE65144 were downloaded from Gene Expression Omnibus (GEO) database. Compared with the normal tissue, GEO2R was conducted to screen the DEGs in each chip under the condition of |log FC| > l, adjusted P‐values (adj. P) < 0.05. The Retrieval of Interacting Genes (STRING) database was used to calculate PPI networks of DEGs with a combined score >0.4 as the cut-off criteria. The hub genes in the PPI network were visualized and selected according to screening conditions in Cytoscape software. In addition, the novel genes in ATC were screened for survival analysis using Kaplan–Meier plotter from those hub genes and validated by RT-qPCR.

Results

A total of 284 overlapping DEGs were obtained, including 121 upregulated and 161 downregulated DEGs. A total of 232 DEGs were selected by STRING database. The 50 hub genes in the PPI network were chosen according to three screening conditions. In addition, the Kaplan–Meier plotter database confirmed that high expressions of ANLN, CENPF, KIF2C, TPX2, and NDC80 were negatively correlated with poor overall survival of ATC patients. Finally, RT-qPCR experiments showed that KIF2C and CENPF were significantly upregulated in ARO cells and CAL-62 cells when compared to Nthy-ori 3–1 cells, TPX2 was upregulated only in CAL-62 cells, while ANLN and NDC80 were obviously decreased in ARO cells and CAL-62 cells.

Conclusion

Our study suggested that CENPF, KIF2C, and TPX2 might play a significant role in the development of ATC, which could be further explored as potential biomarkers for the treatment of ATC.

Identification of Immune Cell Infiltration and Immune-Related Genes in the Tumor Microenvironment of Glioblastomas

Glioblastoma (GBM) is one of the most prevalent malignant brain tumors with poor prognosis. Increasing evidence has revealed that infiltrating immune cells and other stromal components in the tumor microenvironment (TME) are associated with prognosis of GBM. The aim of the present study was to identify immune cells and immune-related genes extracted from TME in GBM. RNA-sequencing and clinical data of GBM were downloaded from The Cancer Genome Atlas (TCGA). Four survival-related immune cells were identified via Kaplan-Meier survival analysis and immune-related differentially expressed genes (DEGs) screened. Functional enrichment and protein-protein interaction (PPI) networks for the genes were constructed. In addition, we identified 24 hub genes and the expressions of 6 of the genes were significantly associated with prognosis of GBM. Finally, the genes were validated in single-cell sequencing studies of GBM, and the immune cells validated in an independent GBM cohort from the Chinese Glioma Genome Atlas (CGGA). Overall, 24 immune-related genes infiltrating the tumor microenvironment were identified in the present study, which could serve as novel biomarkers and immune therapeutic targets.

Identification of TYROBP and C1QB as Two Novel Key Genes With Prognostic Value in Gastric Cancer by Network Analysis

Background: Gastric cancer (GC) is the fifth most frequently diagnosed malignancy, and the third leading cause of tumor-related mortalities worldwide. Due to a high heterogeneity in GC, its treatment and prognosis are challenging, necessitating urgent identification of novel prognostic predictors for GC patients. Methods: We downloaded RNA sequence data, from the Cancer Genome Atlas and microarray data from Gene Expression Omnibus database, then identified common differentially-expressed genes (DEGs) between GC and normal gastric tissues across four datasets. We then used a combination of protein-protein interaction (PPI) network and weighted gene co-expression network analysis (WGCNA) to identify key genes with prognostic value in GC. Thereafter, we used quantitative real time polymerase chain reaction (qRT-PCR) to validate expression of the identified key genes in the Zhejiang University (ZJU) cohort. Finally, we evaluated the relationships between gene expression and immune factors, including immune cells and biomarkers of immunotherapy. Results: Among 426 common DEGs screened, 333 and 93 were upregulated and downregulated, respectively. PPI network and WGCNA successfully identified the top 30 hub genes, among which PTPRC, TYROBP, CCR1, CYBB, LCP2, and C1QB were common. Furthermore, TYROBP and C1QB were negatively associated with prognosis of GC patients, implying that they were key GC predictors. Interestingly, TYROBP and C1QB were positively correlated with predictive biomarkers for GC immunotherapy, including PD-L1 expression, CD8⁺ T cells infiltration, and EBV status. Conclusions: TYROBP and C1QB were identified as two novel key genes with prognostic value in GC by network analysis.

Comprehensive analysis of autophagy-related prognostic genes in breast cancer

Accumulating evidence revealed that autophagy played vital roles in breast cancer (BC) progression. Thus, the aim of this study was to investigate the prognostic value of autophagy-related genes (ARGs) and develop a ARG-based model to evaluate 5-year overall survival (OS) in BC patients. We acquired ARG expression profiling in a large BC cohort (N = 1007) from The Cancer Genome Atlas (TCGA) database. The correlation between ARGs and OS was confirmed by the LASSO and Cox regression analyses. A predictive model was established based on independent prognostic variables. Thus, time-dependent receiver operating curve (ROC), calibration plot, decision curve and subgroup analysis were conducted to determine the predictive performance of ARG-based model. Four ARGs (ATG4A, IFNG, NRG1 and SERPINA1) were identified using the LASSO and multivariate Cox regression analyses. A ARG-based model was constructed based on the four ARGs and two clinicopathological risk factors (age and TNM stage), dividing patients into high-risk and low-risk groups. The 5-year OS of patients in the low-risk group was higher than that in the high-risk group (P < 0.0001). Time-dependent ROC at 5 years indicated that the four ARG-based tool had better prognostic accuracy than TNM stage in the training cohort (AUC: 0.731 vs 0.640, P < 0.01) and validation cohort (AUC: 0.804 vs 0.671, P < 0.01). The mutation frequencies of the four ARGs (ATG4A, IFNG, NRG1 and SERPINA1) were 0.9%, 2.8%, 8% and 1.3%, respectively. We built and verified a novel four ARG-based nomogram, a credible approach to predict 5-year OS in BC, which can assist oncologists in determining effective therapeutic strategies.